Construction of <scp>IIoT</scp> ‐Based Smart Education Environment and Innovation of Practical Teaching Mode for Teacher Training Students

ABSTRACT In view of the challenges faced by traditional teaching models in the context of digital transformation of education, this study proposes to build a smart education environment based on the industrial Internet and innovate the practical teaching mode of normal students. The research adopts hierarchical system architecture to integrate data collection, edge computing and cloud computing technologies, and focuses on optimizing the support vector machine algorithm to achieve educational data classification and anomaly detection, with an accurate rate of 93.7%. Experimental results show that multimodal data fusion improves the analysis accuracy by 15%, and the real‐time feedback delay is controlled within 200 ms, which effectively supports teaching evaluation and behavior analysis.

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Publication Details

Journal
Internet Technology Letters
Published
2025-10-07
DOI
https://doi.org/10.1002/itl2.70163
Primary Topic
Educational Reforms and Innovations
Type
article
Field-Weighted Citation Impact
0.00
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article

Construction of IIoT ‐Based Smart Education Environment and Innovation of Practical Teaching Mode for Teacher Training Students

Rong Xi, Juan Yu
Internet Technology Letters
Educational Reforms and Innovations
article

Construction of IIoT ‐Based Smart Education Environment and Innovation of Practical Teaching Mode for Teacher Training Students

Rong Xi, Juan Yu
article en

Abstract

ABSTRACT In view of the challenges faced by traditional teaching models in the context of digital transformation of education, this study proposes to build a smart education environment based on the industrial Internet and innovate the practical teaching mode of normal students. The research adopts hierarchical system architecture to integrate data collection, edge computing and cloud computing technologies, and focuses on optimizing the support vector machine algorithm to achieve educational data classification and anomaly detection, with an accurate rate of 93.7%. Experimental results show that multimodal data fusion improves the analysis accuracy by 15%, and the real‐time feedback delay is controlled within 200 ms, which effectively supports teaching evaluation and behavior analysis.

Internet Technology LettersVol. 8(6)
Hubei University of Education (CN), Hubei Normal University (CN)
Openalex Percentile: Top 64%
Educational Reforms and Innovations
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